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Determination of Global Minima of Some Common Validation Functions in Support Vector Machine

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2 Author(s)
Jian-Bo Yang ; Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore ; Chong-Jin Ong

Tuning of the regularization parameter C is a well-known process in the implementation of a support vector machine (SVM) classifier. Such a tuning process uses an appropriate validation function whose value, evaluated over a validation set, has to be optimized for the determination of the optimal C. Unfortunately, most common validation functions are not smooth functions of C. This brief presents a method for obtaining the global optimal solution of these non-smooth validation functions. The method is guaranteed to find the global optimum and relies on the regularization solution path of SVM over a range of C values. When the solution path is available, the computation needed is minimal.

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Neural Networks, IEEE Transactions on  (Volume:22 ,  Issue: 4 )